Markers for inflammatory bowel disease

ABSTRACT

There is provided protein biomarkers and methods for their use in diagnosing and treating Inflammatory Bowel Disease (IBD), ulcerative colitis (UC) and Crohn&#39;s disease (CD) as well as methods for assessing the severity of the diseases.

This application is a continuation-in-part of PCT/CA2015/050992 andclaims priority of PCT/CA2015/050992 filed Oct. 2, 2015 designating theUnited States and which claims priority of U.S. provisional application62/059,316 filed on Oct. 3, 2014.

TECHNICAL FIELD

This invention relates generally to protein markers for inflammatorybowel disease (IBD), ulcerative colitis (UC) and Crohn's disease (CD)classification.

BACKGROUND

Inflammatory Bowel Disease encompasses two principal conditions:ulcerative colitis (UC) and Crohn's disease (CD). Some patients havefeatures of both subtypes and are classified as IBD-undefined (IBD-U)(Gastroenterology, 2007. 133(5): p. 1670-89). UC is defined bycontinuous mucosal inflammation starting in the rectum and restricted tothe colon while CD inflammation can occur anywhere in thegastrointestinal tract, involves full thickness of the bowel wall andoften with skip lesions (Gastroenterol Clin North Am, 2009. 38(4): p.611-28; Gastroenterology, 2007. 133(5): p. 1670-89). Recent attempts tofind new markers for IBD subtypes, such as conventional antibodies, havefared very poorly at differentiating colonic CD versus UC. As treatmentsand responses to medical therapies differ between CD and UC (J PediatrGastroenterol Nutr, 2010, S1-S13. The American journal ofgastroenterology, 2011. 106 Suppl 1: p. S2-25; quiz S26. GastroenterolClin North Am, 2009. 38(4): p. 611-28) there is an urgent need forbiomarkers to differentiate between CD and UC.

The primary tool used for both diagnosis and IBD management is endoscopy(World J Gastrointest Endosc, 2012. 4(6): p. 201-11). Endoscopy enablesboth visualization of the mucosa and access for mucosal biopsies todiagnose disease, to define disease extent and activity, and to monitordisease progression. The diagnostic accuracy from colonoscopy rangesfrom 60 to 74% (J Clin Pathol, 2002. 55: p. 955-60). Other diagnosticapproaches include radiological imaging and histological examination ofmucosal biopsies in the differentiation of IBD subtypes (e.gnon-caseating submucosal granuloma). However, 10% of patients (Registry.Dtsch Arztebl Int 2015; 112:121-7) have ambiguous diagnosis using theseapproaches and are instead classified as IBD-unclassified (IBD-U)patients (J Pediatr Gastroenterol Nutr 2014; 58:795-806). Accurate andearly diagnosis is essential for proper disease management. The goal ofIBD treatment is to bring active disease into remission and to preventfollow-up relapse (flare-ups). The choice of treatment depends ondisease subtype (CD versus UC), disease location, severity of disease,disease complications and individual host factors (e.g. nutritional andgrowth status, pubertal status, child's age and size, medicationallergies) (J Pediatr Gastroenterol Nutr, 2010, S1-S13. The Americanjournal of gastroenterology, 2011. 106 Suppl 1: p. S2-25; quiz S26.Gastroenterol Clin North Am, 2009. 38(4): p. 611-28). Current drugtherapies consist of aminosalycylates, immune-modulators,corticosteroids, antibiotics and biological therapies (i.e. anti-TNFαmonoclonal antibodies). The optimum therapeutic regimen for maintaininga disease free state still remains to be determined and theeffectiveness of these drugs significantly differs between CD and UC (JPediatr Gastroenterol Nutr, 2010, S1-S13. The American journal ofgastroenterology, 2011. 106 Suppl 1: p. S2-25; quiz S26. GastroenterolClin North Am, 2009. 38(4): p. 611-28). For example, 5-aminosalicylicacid (5-ASA) drugs are moderately effective at inducing remission andpreventing relapse in mild-to-moderate-active UC, while they are notrecommended in the management of active CD (The American journal ofgastroenterology, 2011. 106 Suppl 1: p. S2-25; quiz S26). There is goodevidence for use of methotrexate as maintenance therapy to preventrelapse in CD however, there is no evidence for its use in UC (TheAmerican journal of gastroenterology, 2011. 106 Suppl 1: p. S2-25; quizS26). Greater doses of anti-TNFα therapies at more frequent intervalsare being just now recognized to be required for successful treatment ofsevere UC as compared to standard treatment protocols in use for CD. Onethird of the cost associated with IBD is due to medical therapies (CCFC.2008, report. p. 1-101) stressing the economic importance of aneffective treatment and thereby an accurate diagnosis.

Genome wide association studies in both adults and pediatric patientshave identified novel IBD-associated genes but only define 25% of thegenetic risk for developing IBD and excepting for very young infants(i.e. <2 years of age), no unique genes have been discovered that definepediatric IBD from adult-onset IBD. IBD is a complex polygenic diseaseinvolving multiple risk gene loci (Nature genetics, 2008. 40(8): p.955-62. Nature genetics, 2009. 41(12): p. 1335-40. Nature genetics,2010. 42(4): p. 332-7). These loci encode genes involved in innate andadaptive immunity, autophagy, and maintenance of epithelial barrierintegrity for those genes that have known function. While these studieshave shown us that multiple pathways are involved in the pathogenesis ofIBD, we remain surprisingly ignorant on the root cause(s) andpathogenesis of IBD.

Protein biomarkers could complement current IBD diagnostic tools byreducing ambiguous diagnosis of IBD, subtype differentiation and mayalso deliver insight into the disease course. Previous studies haveidentified proteins that are elevated and measurable in serum or stool,however the clinical relevance of these proteins in diagnosis of IBD-Upatients is limited, and have been found to perform best in more obviouscases of CD or UC in the pediatric population (Pediatrics 2010;125:1230-6; Inflamm Bowel Dis 2012; 18:1493-7). Serum detectedantibodies directed against neutrophil or bacterial components tend tohave low sensitivities (true positive rate <50%). Other biomarkers arenow becoming available, namely fecal calprotectin, which are clinicallyuseful to identify IBD patients from populations without mucosalinflammation (e.g. irritable bowel syndrome (IBS), healthy controls),but cannot differentiate IBD subtypes (A mini-review. Can JGastroenterol Hepatol 2015; 29:157-63). Fecal calprotectin has notproven to be a good measure to distinguish between mild, moderate orsevere disease (Inflamm Bowel Dis 2012; 18:1493-7) which is important indeciding appropriate therapeutic intervention. There is a clear need fornew approaches that can rapidly and accurately provide an earlydiagnosis of IBD, particularly considering the lack of good genetic andprotein markers, atypical presentations and the often rapid progressionof IBD in the pediatric population.

In view of the above there is a need for better diagnostic methods.

SUMMARY

The invention relates to a method for determining a likelihood ofpresence of IBD disease in a subject comprising the steps of: (A)providing a lower digestive tract biopsy obtained from a subject; B)assessing a level of one or more proteins selected from the group ofinterferon-induced protein 53, arginosuccinate synthase, Annexin 3,calumenin, Serpin H1, interleukin-25 (IL-25), cytosol aminopeptidase(LAPS; gene name and protein name are used interchangeably herein),Superoxide dismutase, S100A8, S100E, S100A9, visfatin (Nicotinamidephosphoribosyltransferase with uniprot ID P43490), and inorganicpyrophosphatase and combination thereof; C) comparing the level with anaverage level of the one or more proteins from normal control subjects;wherein a level of the one or more proteins higher than said averagelevel is indicative of disease.

In another aspect there is also provided a method for determining alikelihood of presence of IBD disease in a subject comprising the stepsof: A) providing a lower digestive tract biopsy obtained from a subject;B) assessing a level of one or more proteins selected from the group of3-hydroxy-3 methylglutarate-CoA lyase; amine oxidase A, Aldo-ketoreductase family member B10, Macropain delta chain, UDP-glucose6-dehydrogenase, Iron-sulfur subunit of complex II, Rhodanese,NADH-ubiquinone oxidoreductase 75 kDa subunit, aconitase 2(mitochondrial), creatinine Kinase B-chain, flavoprotein subunit ofcomplex II, fatty acid binding protein, UDP-glucose 6-dehydrogenase, andleucine-rich PPR motif-containing protein and combination thereof; D)comparing the level with an average level of the one or more proteinsfrom normal control subjects; wherein a level of the one or moreproteins lower than the average level is indicative of disease.

In a further aspect there is provided a method for determining alikelihood of presence of IBD disease in a subject comprisingdetermining the likelihood for fatty acid-binding protein, visfatin,UDP-Glucose 6-dehydrogenase, leucine-rich PRR motif-containing proteinand inorganic pyrophosphatase according to the above described methodsand wherein the disease is present when levels of fatty acid-bindingprotein, visfatin, UDP-Glucose 6-dehydrogenase, leucine-rich PRRmotif-containing protein and inorganic pyrophosphatase are indicative ofdisease

In yet another aspect there is provided a method for determining alikelihood of presence of UC disease in an IBD subject comprising thesteps of: A) providing a lower digestive tract biopsy obtained from asubject; B) assessing a level of one or more proteins selected from thegroup of calumenin, signal recognition particle receptor subunit beta,caldesmon, asparagine synthetase, RING finger protein 71, macropaindelta chain, NADH dehydrogenase[ubiquinone] iron sulfur protein 6,cathepsin S, Fibulin-1, Cell death regulatory protein GRIM-19, cavin 1,protein transport protein Sec61 (Sec61; gene name and protein name areused interchangeably herein), Staphylococcal nuclease domain-containingprotein 1 (SND1; gene name and protein name are used interchangeablyherein), and serotransferrin and combination thereof; C) comparing thelevel with average levels of said one or more proteins from subjectswith CD; wherein a subject with level of said one or more proteinshigher than said average levels is indicative of disease.

In another embodiment of the invention there is provided a method fordetermining a likelihood of presence of UC disease in an IBD subjectcomprising the steps of: A) providing a lower digestive tract biopsyobtained from a subject; B) assessing a level of one or more proteinsselected from the group of carbonate dehydratase II, creatinine kinase Bchain, Galectin-3-binding protein and Fatty acid binding protein,trifunctional enzyme subunit beta (mitochondrial), cytosolaminopeptidase, leukotriene A-4 hydrolase, metallothionein-2 (MT2; genename and protein name are used interchangeably herein), tricarboxylatetransport protein (mitochondrial), heterogeneous nuclearribonucleoprotein H3 (HNRNP H3; gene name and protein name are usedinterchangeably herein), delta(3,5)-delta(2,4)-dienoyl-CoA isomerase(mitochondrial; ECH1; gene name and protein name are usedinterchangeably herein), transferrin receptor protein 1, andbeta-2-microglobulin and combination thereof; C) comparing the levelwith average levels of the one or more proteins from subjects with CD;wherein a subject with level of the one or more proteins lower than theaverage levels is indicative of disease.

There is also provided a method for determining a likelihood of presenceof CD disease in an IBD subject comprising the steps of: A) providing alower digestive tract biopsy obtained from a subject; B) assessing alevel of one or more proteins selected from the group of calumenin,signal recognition particle receptor subunit beta, caldesmon, asparaginesynthetase, RING finger protein 71, protein transport protein Sec61,Staphylococcal nuclease domain-containing protein 1, and serotransferrinand combination thereof; D) comparing the level with average levels ofthe one or more proteins from subjects with UC; wherein a subject withlevel of the one or more proteins lower than the average levels isindicative of disease.

In yet another aspect there is provided a method for determining alikelihood of presence of CD disease in an IBD subject comprising thesteps of: A) providing a lower digestive tract biopsy obtained from asubject; B) assessing a level of one or more proteins selected from thegroup of carbonate dehydratase II, creatinine kinase B chain,Galectin-3-binding pr, Fatty acid binding pr, calcium-activated chloridechannel family member 1, Myristoylated alanine-rich C-kinase substrate,uncharacterized protein C19orf21, CD49 antigen-like family member,carbonate dehydratase II, IG mu chain C region, STAT 3, integrinalpha-6, trifunctional enzyme subunit beta (mitochondrial), cytosolaminopeptidase, leukotriene A-4 hydrolase, metallothionein-2,tricarboxylate transport protein (mitochondrial), heterogeneous nuclearribonucleoprotein H3 (HNRP H3; gene name and protein name are usedinterchangeably herein), delta(3,5)-delta(2,4)-dienoyl-CoA isomerase(mitochondrial; ECH1), transferrin receptor protein 1, andbeta-2-microglobulin and combination thereof; C) comparing the levelwith average levels of the one or more proteins from normal controlsubjects and from subjects with UC; wherein a subject with level of theone or more proteins higher than said average levels is indicative ofdisease.

In another aspect of the invention there is provided a method fordiagnosing a severity of IBD, UC or CD disease comprising measuring alevel of a biomarker protein for a gut (lower digestive tract) sample,assigning a severity score that correlates with a clinical diseaseactivity index.

In another aspect the method for assessing severity is for CD severityand comprises measuring a level of one or more proteins selected fromthe proteins listed in column A of table 3, and/or inorganicphosphatase, visfatin, MT2, calumenin, rhodanese, HSP70, Cytochrome coxidase subunit 5B (COX 5b; gene name and protein name are usedinterchangeably herein), Cytochrome c oxidase subunit 7C (Cox 7C; genename and protein name are used interchangeably herein), NADHdehydrogenase [ubiquinone] flavoprotein 1 and flavoprotein subunit ofcomplex II, correlating with PCDAI disease index.

In yet another aspect the method for assessing severity is for UC andcomprises measuring a level of one or more proteins selected from theproteins listed in column B of table 3 and/or HNRP H3, Myeloid cellnuclear differentiation Ag, galactowaldenase, carnitineO-palmitoyltransferase 1, Sec 11 and calponin H1, and correlating withPUCAI disease activity.

There is also provided a method for treating IBD, UC or CD in a patientcomprising: determining whether said patient has IBD, UC or CD accordingto any one of or combination of the methods described above andadministering to said patient a compound pharmaceutically effectiveagainst said IBD, UC or CD.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood by way of the following detaileddescription of embodiments of the invention with reference to theappended drawings, in which:

FIG. 1 is a chart of the relative expression of 5 proteins in controland IBD subjects;

FIG. 2 is a chart of the relative expression of 12 proteins in CD and UCsubjects;

FIG. 3A is a graph of the relative expression of Inorganic Phosphataseas a function of CD severity score;

FIG. 3B is a graph of the relative expression of visfatin as a functionof CD severity score;

FIG. 3C is a graph of the relative expression of MT-2 as a function ofCD severity score;

FIG. 3D is a graph of the relative expression of HNRP H3 as a functionof UC severity score;

FIG. 4A is a chart of the amount of visfatin in control and IBD subjectsmeasured by ELISA;

FIG. 4B is a chart of the amount of MT2 in CD and UC subjects measuredby ELISA;

FIG. 4C is a graph of the amount of MT2 as a function of PCDAI score;

FIG. 5A-5K are chart of relative ratios of several proteins in control,CD and UC subjects;

FIG. 6 is a chart of the relative ratio of Myeloid cell nucleardifferentiation Ag for control and UC subjects with different levels ofseverity;

FIG. 7A is an immunoblot of validated biomarkers for pediatric IBDdisease;

FIG. 7B is bar graph of relative densitometry of Calumenin in control,CD, UC subjects;

FIG. 7C is a chart of levels of Calumenin in control, CD, UC subjects;

FIG. 7D is bar graph of relative densitometry of LAP3 in control, CD, UCsubjects;

FIG. 7E is a chart of levels of LAP3 in control, CD, UC subjects;

FIG. 7F is bar graph of relative densitometry of B-CK in control, CD, UCsubjects;

FIG. 7G is a chart of levels of B-CK in control, CD, UC subjects;

FIG. 8 A-E are charts of relative ratios of proteins identified asbiomarkers to distinguish UC and CD disease.

FIG. 9 A-D are charts of relative ratios of proteins identified asbiomarkers for the severity of UC.

FIG. 10 A-G are charts of relative ratios of proteins identified asbiomarkers for the severity of CD.

FIG. 11 A-R are charts of relative ratios of proteins identified asbiomarkers for distinguishing UC and CD using PCA analysis.

FIG. 12 A-I are charts of relative ratios of proteins identified asbiomarkers for distinguishing UC and CD using PCA analysis.

FIG. 13 A-I are charts of relative ratios of proteins identified asbiomarkers for distinguishing UC and CD using Roccet analysis.

FIG. 14 A-L are charts of relative ratios of proteins identified asbiomarkers for distinguishing UC and CD using PLSDA analysis showinghigh level CD compare to UC.

FIG. 15 A-K are charts of relative ratios of proteins identified asbiomarkers for distinguishing UC and CD using PLSDA analysis showinghigh level UC compare to CD.

FIG. 16 A-G are charts of relative ratios of proteins identified asbiomarkers for distinguishing control and IBD using PLSDA analysis.

FIG. 17 A-J are charts of relative ratios of proteins identified asbiomarkers for distinguishing control, UC and CD using PLSDA analysis.

FIG. 18 A-K are charts of normalized ratios of proteins identified asbiomarkers for distinguishing control, IBD, UC and CD.

DETAILED DESCRIPTION

The invention will be better understood by way of the following detaileddescription of embodiments of the invention with reference to theappended drawings and tables.

There is provided proteins markers and methods of using these markers toidentify patients with IBD disease as well as to classify IBD diseaseinto underlying conditions (sub-types) namely UC and CD. There is alsoprovided a method for assessing the severity of disease.

By severity of the disease it is meant a level of symptoms as describedin disease activity index such Crohn's disease activity index (CDAI),Pediatric Crohn's disease activity index (PCDAI) Harvey-Bradshaw index,Ulcerative colitis activity index (UCAI), Pediatric Ulcerative colitisactivity index (PUCAI), Paris classification of pediatric Crohn'sdisease and the like. For example severe CD corresponds to a score of450 in the CDAI index.

By patients having Inflammatory Bowel Disease (IBD) it is meant patientswith ulcerative colitis (UC) or patients with Crohn's disease (CD) orIBD-undefined (IBD-U).

In one embodiment lower digestive tract biopsies such as colon biopsieswere obtained from pediatric patients at the time of diagnostic andprior to therapeutic intervention. Using a super-SILAC-based approach(described further below), the proteomes of non-IBD control, CD, and UCpatient biopsies were compared. Biomarker candidates can be identifiedby classification/regression methods such as Partial Least SquaresDiscriminant Analyses (PLS-DA), Support Vector Machine (SVM) and RandomForest (RF), ANOVA, t-test, linear regression, and principle componentanalysis. These methods can be applied to identify proteins that arespecific to each disease state. Paired comparisons of proteomes frompatient biopsies obtained from non- or inflamed areas of the colon (CoNand CoA respectively) can be employed to identify additional biomarkersof disease severity.

In an aspect of the invention there is provided a method in which IBDcan be detected by measuring the levels (or relative abundance) ofcertain proteins in samples from the gut of patients. Samples from thegut may be obtained from intestinal mucosal biopsies, gut lavage orcombination thereof.

In one embodiment of the invention, gut lavage can be performed duringendoscopy by flushing a physiological solution, such as sterile salinesolution or sterile water, onto the mucosa to remove the stronglyadherent mucus layer overlying the intestinal mucosal epithelial cellsand the microbial community embedded within the mucus layer. Aspiratesare then collected directly through a colonoscope at a specific locationin the gut as for example from the terminal ileum, right colon, and leftcolon and the samples are preferably immediately put on ice right in theendoscopy suite. For example the following steps can be performed: 1) aregular protocol of bowel clean out in preparation for colonoscopy isfirst applied to the patient, 2) then the colonoscope (“scope”) isadvanced to the ascending colon or a region of the colon distal to thatof interest, 3) suction out fluid and particulate matter, using eitherthe scope's wash system or with a syringe through biopsy port, 4) flushsterile water onto mucosa until shards of mucus are dislodged, 5)aspirate mucus containing fluid into sterile trap through scopeaspiration system, 6) remove the trap from scope suction and cap it andimmediately place on ice, 7) advance the scope to more proximal regionof interest and repeat steps 3-6, 8) traps with mucus are placed on iceuntil further processing. The sample can then be analyzed at the pointof care or transferred to a laboratory. The samples can also be furtherprocessed and then stored at −80° C.

Biopsies can be obtained by procedures that are well known in the artand can be obtained from region of the colon that are macroscopicallyinflamed or not.

Proteins can be indentified and quantified by techniques known in theart such as shotgun mass-spectrometry in conjunction with proteinfractionation. Other method for detecting specific proteins such as,immunology based methods (antibodies), western blots, spectrophotometry,enzyme assays, ELISA and any other method as would be known to oneskilled in the art may also be used.

Analysis of the data can be performed using for example proteomicsoftware packages such as the MaxQuant software and using software suchas, but not limited to, Perseus, matlab, Roccet and R for validation andstatistical analysis.

In one embodiment of the invention, the presence of IBD disease in asubject can be assessed by the relative abundance of certain hostproteins. In this respect it is shown that certain proteins exhibit adifference in their relative abundance in individuals with UC or CDdisease relative to healthy (IBD-free, also referred to as controls)individuals and therefore indicate the presence of IBD.

In another embodiment of the invention CD and UC disease can bedistinguished in IBD patients by determining the relative abundance ofcertain host proteins. In this respect, it is shown that certainproteins exhibit a difference in their relative abundance in individualswith UC vs individuals with CD and therefore these proteins can be usedas markers to distinguish between CD and UC.

In yet another embodiment of the invention the severity of UC diseasecan be assessed by the relative abundance of certain host proteins. Inthis respect it is shown that certain proteins exhibit a difference intheir relative abundance with respect to controls in individuals withmild, moderate or severe UC disease.

The invention provides a method in which the severity of CD disease canbe assessed by the relative abundance of certain host proteins. In thisrespect it is shown that certain proteins exhibit a difference in theirrelative abundance with respect to controls in individuals with mild,moderate or severe CD disease.

It will be appreciated that a subject's diagnosis can be achieved bymeasuring the levels of one or more protein markers and by comparingthese levels to average levels of the one or more markers in controlsand/or disease groups that have been previously acquired and analyzed.It will be further appreciated that several markers may be combined forexample to increase the statistical significance or accuracy or thediagnosis or to reduce the number of false positives or false negativesand the like. Furthermore it will be appreciated that ratios of relativeabundance between markers can also be derived that are indicative ofpresence, type and severity of disease.

The differences in the relative abundance of proteins in individualswere assessed using different statistical models. It will be appreciatedthat the choice of an appropriate statistical model may depend on thesize of the samples, distributions of experimental values, the outcomebeing tested and any other factors affecting the relevance of aparticular model. It will further be appreciated that certain proteinmarkers may be identified as such by a certain statistical model but notanother. In other words certain statistical models may have sufficientdiscrimination power while others may not. Furthermore within a samemodel discrimination power may vary depending on the test parameters.

There is also provided a method for assessing the severity of thedisease by measuring an amount or a relative amount of one or moreproteins to provide a clinical index correlation number. The presentinvention established that the abundance or relative abundance ofcertain proteins correlate with the severity of disease, in particularUC or CD disease as determined by clinical disease activity indexes suchas PUCAI or PCDAI. Therefore this correlation enables the establishmentof a clinical correlation index number using the measured abundance orrelative abundance of certain proteins as will be further describedbelow.

The above methods for identifying IBD, UC and CD disease, or theseverity of the disease enable the establishment of more specific,timely and efficient treatment protocols for patients. The treatmentprotocols are well known by health professionals when the diagnosis isestablished. However, as mentioned above such diagnoses are sometimesdifficult to make. The methods described above to establish diagnosiscan therefore be advantageously relied on to determine appropriatetreatment protocols.

IBD in general and UC and CD disease can be treated usingpharmaceutically acceptable amounts of one or more compounds selectedfor example from the group of aminosalycylates, immunomodulators,anti-integrins, anti-cytokines, enteral feed programs, steroids,corticosteroids, antibiotics, anti-TNFa, bismuth or a combinationthereof.

However, knowing the type, stage and severity of the disease is crucialin determining the optimal treatment. For example, mild UC may benefitfrom aminosalicylates treatment while severe UC may be more responsiveto immunomodulators.

EXAMPLES Example 1

Material and Methods

Subjects Selection and Sampling:

All patients under 18 years of age and scheduled to undergo diagnosticcolonoscopy were considered eligible for recruitment. Exclusioncriteria, related to conditions known to affect mucosal gene expression,included: (1) a body mass index greater than the 95^(th) percentile forage; (2) diabetes mellitus (insulin and non-insulin dependent); (3)infectious gastroenteritis within the preceding 2 months; (4) use of anyantibiotics or probiotics within the last 4 weeks; or (5) IBS. Thesesame exclusion criteria were applied to the non-IBD control group. AllIBD cases met the standard diagnostic criteria for either ulcerativecolitis (UC) or Crohn's disease (CD) following thorough clinical,microbiologic, endoscopic, histologic and radiologic evaluation (JPediatr Gastroenterol Nutr 2007; 44:653-74). Phenotyping of disease wasbased on endoscopy and clinical disease activity scores and recordedutilizing the Paris modification of the Montreal Classification for IBD(Inflamm Bowel Dis 2011; 17:1314-21). Clinical disease activity of CDwas determined using the Pediatric Crohn's Disease Activity Index(PCDAI)(J Pediatr Gastroenterol Nutr 2005; 41:416-21) and of UC usingthe Pediatric Ulcerative Colitis Activity Index (PUCAI)(Gastroenterology2007; 133:423-32). All controls had a macroscopically and histologicallynormal mucosa, and did not carry a diagnosis for any known chronicintestinal disorder (e.g. celiac disease, eosinophilic enterocolitis,IBS). Ascending colon and terminal ileum is the most common site of CD,and pancolitis is common in children with UC (Isr Med Assoc J 2000;2:598-600); the ascending colon was chosen as the site for mucosalbiopsy to eliminate the region of the bowel biopsied as a confounder. Assuch, only patients from whom ascending colon biopsies were obtainedwere included in the proteomic study.

The study was approved by the Research Ethics Board of the Children'sHospital of Eastern Ontario (CHEO). Subject clinical data were collectedand managed using Research Electronic Data Capture (REDCap) (J BiomedInform 2009; 42:377-81) hosted at the CHEO Research Institute.

Sample Processing and Analyses:

Briefly, frozen biopsies were lysed by mechanical homogenization andproteins isolated following centrifugation. 45 μg of sample protein wascombined with an equal amount of isotopically-labeled reference proteinlysate to permit for relative quantification of proteins. Trypticdigestion of proteins were performed with filter-aided samplepreparation (Nat Methods 2009; 6:359-62.), and resulting peptidesanalyzed on an Orbitrap Elite mass spectrometer (MS). All MS raw fileswere analyzed in a single run with MaxQuant version 1.5.1, against thehuman Uniprot database (Version Human_20140711). Data filtering andstatistical analysis were performed in Perseus, Excel (Microsoft), andPrism (Graphpad).

Mathematical models of the classification of disease states weredeveloped with a proteomic data from a subset of the patients (discoverycohort), and the models substantiated with data from the remainingpatients (validation cohort). Patient biopsies were randomly dividedinto equal groups between the discovery and the validation cohorts usinga balanced stratification approach for gender and diagnosis (Etcetera inWinPepi, BixtonHealth.ca). Candidate biomarker selection was performedby Partial Least Squares Discriminant Analyses (PLS-DA), Support VectorMachine (SVM) and Random Forest (RF) on the discovery cohort datasetwith ROC Curve Explorer and Tester (ROCCET)(Metabolomics 2013;9:280-299). For each model, the performance was tested with repeatedrandom sub-sampling cross validation wherein ⅔ of the samples where usedfor training and ⅓ for testing, with 50 permutations. Ultimately, thecandidate biomarkers that were selected were identified as significantin all three models, and ranked by the Area Under the Receiver OperatorCurve (AUROC) value. Candidate biomarker panels were developed in theROC Curve Tester module of ROCCET by iterative analysis with a PLSDAmodel using a step-forward method, with candidate biomarkers added byprotein-specific AUROC values. The minimal number of proteins selectedfor inclusion in the panel was based upon the point of plateau for theROC AUC, specificity and sensitivity. Biomarker panels wereindependently validated by applying the validation cohort data to thediscovery-trained PLSDA models.

The discovery cohort PCDAI or PUCDAI scores for CD and UC, respectively,were compared with all proteins in the Q95+ subgroup specific proteinsto determine the Pearson correlation (Graphpad, Prism). Pathway analyseswere performed using Panther (Pantherdb.org) and visualized with iPATH2interactive pathways explorer (pathways.embl.de) using uniprot accessionnumbers. Enzyme linked immunosorbent assays (ELISAs) for visfatin (EznoLife Sciences, NY, USA) and metallothionein-2 (Cloud-Clone Corp., TX,USA) were performed as per the manufacturers protocol on biopsy lysatediluted to a final SDS concentration of 0.08%.

Results

Subjects

Children undergoing diagnostic colonoscopy were recruited for thisproteomic study. Briefly, over the course of 3 years, ascending colonbiopsies were obtained from 101 patients that met the study criteria.The mean age of IBD patients was 13.6±0.4 years (n=61, range 4.8-17.8),and of the controls was 14.4±0.5 years (n=40, range 6.1-17.7), and werecomparable between groups. No gender bias was observed within control orUC patients. A greater percentage of male CD patients than females wererecruited. This gender bias is characteristic for CD in pediatricpopulations (Nat Rev Gastroenterol Hepatol 2014; 11:88-98). The majorityof CD patients (83.3%) had active inflammatory colonic/ileocolonicdisease; 86.7% of UC patients exhibited pancolitis.

Evaluation of Full Proteomic Data Set:

101 biopsies were processed over a 15-month period and analyzed byHPLC-ESI-MSMS to identify and quantify proteins that are differentiallyexpressed between disease conditions. One biopsy was rejected from theanalysis. The remaining samples showed consistent MS profiles over time.

From the 100 remaining patient biopsies included for analyses, 3583proteins were identified by ≥2 unique peptides, 948 of which werequantified in ≥95% of the biopsies (Q95). There were 66 proteinsconsidered to be subgroup specific due to the overrepresentation in onesubgroup (>70% of subgroup biopsies) when compared with at least oneother subgroup (<50% of subgroup biopsies). Principal component analysis(PCA) was performed to test whether the proteomics results couldsegregate patients with different disease status. To limit the effectsdue to imputation of missing data, only the data from the Q95 and thesubgroup specific proteins were used. Using these 1014 proteins, controland IBD proteomes are distinguished by PCA. Interestingly, groupsegregation was also obtained even when proteins annotated as involvedin immunological response were removed from the dataset. Consistent withprevious studies, blood based parameters (Hemoglobin, Albumin, C-reativeprotein (CRP), erythrocyte sedimentation rate (ESR)) were insufficientto segregate patients by PCA analysis.

Establishment of Biomarker Models:

Control Vs. IBD

To determine the minimal subset of proteins that can segregate IBD fromcontrol patients, analysis was performed on the discovery cohort withROC Curve Explorer and Tester (ROCCET)(Metabolomics 2013; 9:280-299).Briefly, control proteomes were compared with IBD (combined CD and UC)proteomes in the multivariate ROC curve explorer module (Metabolomics2013; 9:280-299) using SVM, PLSDA and RF. There were 106 proteins commonto all three models (Table 1).

TABLE 1 ATP-binding cassette sub-family D member 3 6-phosphogluconatedehydrogenase, decarboxylating Nicotinamide phosphoribosyltransferaseHeat shock 70 kDa protein 1A/1B Deleted in malignant brain tumors 1protein Phosphatidylethanolamine-binding protein 1; Hippocampalcholinergic neurostimulating peptide Protein ERGIC-53 Peroxiredoxin-4ATP synthase protein 8 Glycogen phosphorylase, brain form Thioredoxindomain-containing protein 5 Acetyl-CoA acetyltransferase, mitochondrialTrifunctional enzyme subunit beta, mitochondrial; 3-ketoacyl-CoAthiolase Plastin-1 Protein S100-A11; Protein S100-A11, N-terminallyprocessed Villin-1 Cytoskeleton-associated protein 4 Cytochrome b-c1complex subunit 6, mitochondrial Calponin-2 Lactotransferrin;Lactoferricin-H; Kaliocin-1; Lactoferroxin-A; Lactoferroxin-B;Lactoferroxin-C Thiosulfate sulfurtransferase Neutrophil elastaseCytochrome c oxidase subunit 6C Unconventional myosin-IdGamma-interferon-inducible protein 16 Normal mucosa ofesophagus-specific gene 1 protein Four and a half LIM domains protein 1Major vault protein Fumarate hydratase, mitochondrial Serpin H1Filamin-B 78 kDa glucose-regulated protein N(G),N(G)-dimethylargininedimethylaminohydrolase 1 Proteasome activator complex subunit 1Phosphoserine aminotransferase Nucleobindin-2; Nesfatin-1 Creatinekinase B-type Selenium-binding protein 1 SWI/SNF-relatedmatrix-associated actin-dependent regulator of chromatin subfamily Amember 5 Mesencephalic astrocyte-derived neurotrophic factorCalreticulin Protein S100-P SRA stem-loop-interacting RNA-bindingprotein, mitochondrial Electron transfer flavoprotein subunit betaPolymerase I and transcript release factor Ig kappa chain C regionSuperoxide dismutase [Mn], mitochondrial Cytosol aminopeptidaseEpithelial cell adhesion molecule 7-dehydrocholesterol reductase2,4-dienoyl-CoA reductase, mitochondrial Adenosylhomocysteinase;Putative adenosylhomocysteinase 3 Protein disulfide-isomeraseLithostathine-1-alpha Guanine nucleotide-binding protein G(I)/G(S)/G(O)subunit gamma-5 Endoplasmin Fatty acid-binding protein, epidermalPlastin-3 Cytochrome b-c1 complex subunit 7 Succinate dehydrogenase[ubiquinone] iron-sulfur subunit, mitochondrial Aconitate hydratase,mitochondrial Myeloid cell nuclear differentiation antigen Inorganicpyrophosphatase HLA class I histocompatibility antigen, A-24 alpha chainCreatine kinase U-type, mitochondrial Succinyl-CoA ligase [ADP-forming]subunit beta, mitochondrial Carboxypeptidase; Lysosomal protectiveprotein; Lysosomal protective protein 32 kDa chain; Lysosomal protectiveprotein 20 kDa chain Annexin A3; Annexin Transmembrane emp24domain-containing protein 9 Very long-chain specific acyl-CoAdehydrogenase, mitochondrial Galectin-4; Galectin NAD-dependent malicenzyme, mitochondrial Protein NipSnap homolog 1 Vigilin 3-ketoacyl-CoAthiolase, mitochondrial Acyl-CoA synthetase family member 2,mitochondrial Ig gamma-1 chain C region Proteasome subunit beta type-6CD9 antigen NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit13 Leucine-rich PPR motif-containing protein, mitochondrial HistoneH1.0; Histone H1.0, N-terminally processed UDP-glucose 6-dehydrogenaseElectron transfer flavoprotein subunit alpha, mitochondrialBeta-2-microglobulin; Beta-2-microglobulin form pI 5.3 Integrin beta-2;Integrin beta Zyxin Succinate dehydrogenase [ubiquinone] flavoproteinsubunit, mitochondrial Basigin Carbonyl reductase [NADPH] 1 CalpastatinEstradiol 17-beta-dehydrogenase 2 Alpha-2-macroglobulin CD44 antigenProteasome activator complex subunit 2 Junction plakoglobin Cell surfaceA33 antigen Transgelin Keratin, type I cytoskeletal 18 Retinaldehydrogenase 1 Cathepsin Z Alcohol dehydrogenase 1C Mucin-2 Chlorideanion exchanger Tryptophan--tRNA ligase, cytoplasmic; T1-TrpRS; T2-TrpRSVesicular integral-membrane protein VIP36

To identify the minimal number and the particular proteins required forcontrol vs IBD segregation, a PLSDA model was evaluated in the Testermodule of ROCCET. By step-forward analysis, a peak and stabilization ofthe AUC, specificity and sensitivity was observed with five proteins.The relative expressions of these 5 proteins is shown in FIG. 1, wassufficient to differentiate IBD patients from controls with an AUC of1.0 (95% Cl 1.0-1.0), and a classification accuracy of 94.5%.

CD Vs. UC

From the 15 CD and 15 UC proteomes included in the discovery cohort forsub-classification, a total of 956 from the 1024 possible proteins wereidentified, though just over 26% (252) were common to the three modelsemployed, namely SVM, PLSDA and RF (table 2).

TABLE 2 Protein transport protein Sec61 subunit alpha isoform 1 Cytosolaminopeptidase Staphylococcal nuclease domain-containing protein 1Leukotriene A-4 hydrolase Trifunctional enzyme subunit beta,mitochondrial; 3-ketoacyl-CoA thiolase Metallothionein-2Peroxiredoxin-5, mitochondrial ATP synthase subunit beta, mitochondrial;ATP synthase subunit beta Heterogeneous nuclear ribonucleoprotein H3Thymosin beta-10 Heat shock 70 kDa protein 1A/1B SerotransferrinDelta(3,5)-Delta(2,4)-dienoyl-CoA isomerase, mitochondrialTricarboxylate transport protein, mitochondrial Aminopeptidase BTryptophan--tRNA ligase, cytoplasmic; T1-TrpRS; T2-TrpRS Transferrinreceptor protein 1; Transferrin receptor protein 1, serum form3-beta-hydroxysteroid-Delta(8),Delta(7)-isomerase Vigilin Proto-oncogenetyrosine-protein kinase Src Filamin-C Histone H1.0; Histone H1.0,N-terminally processed S-formylglutathione hydrolaseTranslocon-associated protein subunit delta Neuroblastdifferentiation-associated protein AHNAK Calumenin Ras-related proteinRab-1B NADH dehydrogenase [ubiquinone] iron-sulfur protein 2,mitochondrial Acyl-CoA-binding protein 6-phosphogluconolactonase Hypoxiaup-regulated protein 1 Fibrinogen alpha chain; Fibrinopeptide A;Fibrinogen alpha chain Protein kinase C and casein kinase substrate inneurons protein 2 Bone marrow proteoglycan; Eosinophil granule majorbasic protein Beta-2-microglobulin; Beta-2-microglobulin form pI 5.3Glutathione reductase, mitochondrial Coronin-1B; Coronin Guaninenucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-5 Vacuolarprotein sorting-associated protein 29 Palladin Aconitate hydratase,mitochondrial Myristoylated alanine-rich C-kinase substrate ATP synthasesubunit d, mitochondrial U1 small nuclear ribonucleoprotein A Eosinophilcationic protein Fatty acid-binding protein, epidermal Signal transducerand activator of transcription 1-alpha/beta; Signal transducer andactivator of transcription Flavin reductase (NADPH) Calcyclin-bindingprotein Creatine kinase B-type ATP synthase subunit epsilon,mitochondrial; ATP synthase subunit epsilon-like protein, mitochondrialOCIA domain-containing protein 2 Actin-related protein 2/3 complexsubunit 5 Dihydropteridine reductase Programmed cell death protein 5Protein canopy homolog 2 Glycerol-3-phosphate dehydrogenase,mitochondrial Sorting nexin-3 Aldo-keto reductase family 1 member C3Vinculin Cysteine and glycine-rich protein 1 Histone H1x Extendedsynaptotagmin-1 Aflatoxin B1 aldehyde reductase member 2 Transmembraneemp24 domain-containing protein 9 Signal recognition particle subunitSRP72 Ig gamma-3 chain C region Desmin Spermidine synthase Nicotinamidephosphoribosyltransferase Tropomyosin alpha-4 chain Laminin subunitgamma-1 Integrin-linked protein kinase Destrin 2,4-dienoyl-CoAreductase, mitochondrial Endothelial differentiation-related factor 1Medium-chain specific acyl-CoA dehydrogenase, mitochondrial Acyl-proteinthioesterase 1 Protein transport protein Sec23B Filamin-AMicrotubule-associated protein; Microtubule-associated protein 4 PC4 andSFRS1-interacting protein 7-dehydrocholesterol reductase Signalpeptidase complex subunit 2 Myosin light chain kinase, smooth muscle;Myosin light chain kinase, smooth muscle, deglutamylated formTransforming growth factor-beta-induced protein ig-h3 NAD(P)transhydrogenase, mitochondrial Cathepsin B; Cathepsin B light chain;Cathepsin B heavy chain Hydroxyacyl-coenzyme A dehydrogenase,mitochondrial Amine oxidase [flavin-containing] A Spermine synthaseHistone H1.4 Nck-associated protein 1 DNA replication licensing factorMCM7 Glutaredoxin-1 Cytochrome c oxidase subunit 4 isoform 1,mitochondrial Integrin beta-4 PDZ and LIM domain protein 1 Myosin lightchain 1/3, skeletal muscle isoform; Myosin light chain 3Carboxypeptidase; Lysosomal protective protein; Lysosomal protectiveprotein 32 kDa chain; Lysosomal protective protein 20 kDa chainERO1-like protein alpha V-type proton ATPase subunit E 1 CD44 antigenRibosomal L1 domain-containing protein 1 Basement membrane-specificheparan sulfate proteoglycan core protein; Endorepellin; LG3 peptideTryptase alpha/beta-1; Tryptase beta-2 Copine-1 Peptidyl-prolylcis-trans isomerase FKBP2; Peptidyl-prolyl cis-trans isomerase DnaJhomolog subfamily B member 1 Collagen alpha-2(VI) chain Rho-associatedprotein kinase 2 Dihydrolipoyllysine-residue succinyltransferasecomponent of 2-oxoglutarate dehydrogenase complex, mitochondrialMitochondrial-processing peptidase subunit beta Myosin-11 Replicationprotein A 32 kDa subunit Four and a half LIM domains protein 2 Aldehydedehydrogenase, mitochondrial NADH-ubiquinone oxidoreductase 75 kDasubunit, mitochondrial Unconventional myosin-Ib Zyxin Junctionplakoglobin IgGFc-binding protein Ig alpha-1 chain C regionArgininosuccinate synthase Dipeptidyl peptidase 3 Tropomodulin-3 Myosinregulatory light chain 12A; Myosin regulatory light chain 12B NADHdehydrogenase [ubiquinone] 1 beta subcomplex subunit 4 Ribosomematuration protein SBDS Proteasome subunit beta type-8 Superoxidedismutase [Mn], mitochondrial HLA class I histocompatibility antigen,A-24 alpha chain Protein S100-P Coactosin-like protein Serine/argininerepetitive matrix protein 2 SH3 domain-binding glutamic acid-rich-likeprotein Vesicle-trafficking protein SEC22b NEDD8-conjugating enzymeUbc12 Succinate dehydrogenase [ubiquinone] iron-sulfur subunit,mitochondrial Desmoplakin Succinyl-CoA: 3-ketoacid coenzyme Atransferase 1, mitochondrial Fibrinogen beta chain; Fibrinopeptide B;Fibrinogen beta chain Actin-related protein 2/3 complex subunit 3Protein-glutamine gamma-glutamyltransferase 2 Sulfide: quinoneoxidoreductase, mitochondrial Haptoglobin; Haptoglobin alpha chain;Haptoglobin beta chain Pyruvate carboxylase, mitochondrialN(G),N(G)-dimethylarginine dimethylaminohydrolase 2 Creatine kinaseU-type, mitochondrial Polymerase I and transcript release factorEpididymal secretory protein E1 Alpha-2-macroglobulin Transmembraneemp24 domain-containing protein 7 Fibrillin-1 Phosphoserineaminotransferase Purine nucleoside phosphorylase SUMO-activating enzymesubunit 2 Cytoplasmic aconitate hydratase Transcription factor A,mitochondrial Isovaleryl-CoA dehydrogenase, mitochondrial ProteinS100-A8; Protein S100-A8, N-terminally processed Deoxyuridine5-triphosphate nucleotidohydrolase, mitochondrial Protein CDV3 homologZymogen granule membrane protein 16 Four and a half LIM domains protein1 Polymeric immunoglobulin receptor; Secretory componentHydroxymethylglutaryl-CoA synthase, cytoplasmic Fascin Ras-relatedprotein Rab-2A Moesin Prelamin-A/C; Lamin-A/C Ig gamma-1 chain C regionGalectin-3; Galectin Heterogeneous nuclear ribonucleoprotein U-likeprotein 1 Caldesmon NADH dehydrogenase [ubiquinone] 1 alpha subcomplexsubunit 13 Aminopeptidase N Cytochrome b-c1 complex subunit 8Galectin-4; Galectin Aldo-keto reductase family 1 member B10N(G),N(G)-dimethylarginine dimethylaminohydrolase 1 Histone H1.3N-alpha-acetyltransferase 15, NatA auxiliary subunit Plastin-1Complement C3; Complement C3 beta chain; C3-beta-c; Complement C3 alphachain; C3a anaphylatoxin; Acylation stimulating protein; Complement C3balpha chain; Complement C3c alpha chain fragment 1; Complement C3dgfragment; Complement C3g fragment; Complement C3d fragment; ComplementC3f fragment; Complement C3c alpha chain fragment 2 Cyclin-dependentkinase 1 Adenosylhomocysteinase; Putative adenosylhomocysteinase 3Estradiol 17-beta-dehydrogenase 11 Plastin-3 3-ketoacyl-CoA thiolase,mitochondrial Glutathione S-transferase kappa 1 Prosaposin; Saposin-A;Saposin-B-Val; Saposin-B; Saposin-C; Saposin-D Receptorexpression-enhancing protein 5 Leukocyte elastase inhibitor ProbableATP-dependent RNA helicase DDX46 Collagen alpha-2(I) chaincAMP-dependent protein kinase type II-alpha regulatory subunitMetastasis-associated protein MTA2 Bifunctional 3-phosphoadenosine5-phosphosulfate synthase 2; Sulfate adenylyltransferase;Adenylyl-sulfate kinase Deoxynucleoside triphosphate triphosphohydrolaseSAMHD1 Ornithine aminotransferase, mitochondrial; Ornithineaminotransferase, hepatic form; Ornithine aminotransferase, renal formCathepsin G Desmoglein-2 DNA replication licensing factor MCM4Selenium-binding protein 1 Alcohol dehydrogenase 1C Transmembraneprotein 109 Thymosin beta-4; Hematopoietic system regulatory peptideMagnesium transporter protein 1 Ig lambda-2 chain C regions Nuclear porecomplex protein Nup205 GDP-mannose 4,6 dehydratase Hemoglobin subunitdelta Caspase; Caspase-1; Caspase-1 subunit p20; Caspase-1 subunit p10UPF0556 protein C19orf10 UDP-glucose: glycoprotein glucosyltransferase 1Apolipoprotein A-I; Proapolipoprotein A-I; Truncated apolipoprotein A-IEosinophil peroxidase; Eosinophil peroxidase light chain; Eosinophilperoxidase heavy chain Coronin-1A; Coronin Quinone oxidoreductaseProtein S100-A9 Hemoglobin subunit alpha Serpin B6 Immunoglobulin Jchain Calpastatin CD59 glycoprotein Thymidine phosphorylase Nuclearubiquitous casein and cyclin-dependent kinase substrate 1Alpha-1-antitrypsin; Short peptide from AAT Granulins; Acrogranin;Paragranulin; Granulin-1; Granulin-2; Granulin-3; Granulin-4;Granulin-5; Granulin-6; Granulin-7 Anterior gradient protein 2 homologDolichyl-diphosphooligosaccharide--protein glycosyltransferase subunitSTT3B Transgelin Macrophage-capping protein Myosin-14 Tubulin alpha-4Achain Collagen alpha-1(VI) chain Serpin H1 Mucin-2 D-3-phosphoglyceratedehydrogenase Unconventional myosin-Id Cystatin-B Nucleolar RNA helicase2 RNA-binding protein 39 Neutrophil defensin 3; HP 3-56; Neutrophildefensin 2 Succinyl-CoA ligase [GDP-forming] subunit beta, mitochondrialADP/ATP translocase 3; ADP/ATP translocase 3, N-terminally processedVasodilator-stimulated phosphoprotein Calcium-activated chloride channelregulator 1 Fibrinogen gamma chain Probable ATP-dependent RNA helicaseDDX23

Step forward analysis of the 252 proteins was applied to the PLSDA modelto identify the minimal number and candidate biomarker proteins requiredfor segregation of CD from UC. Points of inflection were observed in theAUC with 3, 5, 8, and 10 proteins. A plateau in specificity andsensitivity was observed at 12 proteins, and thus determined to be theminimal number of proteins required for optimal classification. Therelative expression of the 12 proteins is shown (FIG. 2). Notably,beta-2-microglobulin was not significantly different between CD and UCgroups after FDR adjustment (p=0.0703), though contributes to thespecificity and sensitivity of the panel (FIG. 1D). The panel of 12proteins resulted in an overall AUC of 0.958 (95% Cl 0.84-1.0), with asensitivity and specificity of 1.0 and 0.933 respectively.

Application and performance evaluation of the panels to an independentvalidation cohort:

As outlined, independent validation of the biomarker panels PLSDA modelswere accomplished by assessment of the proteomic data from thevalidation cohort. Proteins of FIG. 1 applied to the classification ofthe validation cohort result in an AUC of 0.997, with 48/50 patientsaccurately classified as either control or IBD as determined by ROCanalysis. Similarly, the 12 proteins of FIG. 2 differentiate CD from UCwith an AUC of 0.862, with 24 of 30 patients accurately classified. PCAperformed using the proteins of FIG. 2 shows good separation of the CDand UC populations. Despite reduced sensitivity and specificity in thevalidation cohort compared with the discovery group, these resultsindicate the utility of the biomarker panels in diagnosis andsub-diagnosis of IBD patients.

Candidate Biomarkers are Biologically Relevant.

Pathway analysis was performed to evaluate the functional roles of the106 IBD and 252 differential diagnostic candidate biomarkers. Themajority of proteins that segregate IBD from control are involved inmetabolic processes, and function predominantly in catalysis,specifically oxidoreductase activity. Canonical pathways identified todiffer in IBD are related to energy metabolism. Proteins elevated in CDare related to fatty acid metabolism whereas proteins elevated in UCfunction in energy metabolism.

Correlation with Severity:

Pearson correlation was calculated on the 945 Q95+subgroup specificproteins in the discovery cohort with the severity of the disease basedon the PCDAI/PUCAI patient scores. In total, 118 proteins correlatedsignificantly with PCDAI or PUCAI (table 3).

TABLE 3 Correlation with CD severity; Correlation with UC severity;Column A Column B Inorganic pyrophosphatase Caldesmon CaldesmonHeterogeneous nuclear ribonucleoprotein U-like protein 2 Heterogeneousnuclear Integrin-linked protein kinase ribonucleoprotein U-like protein2 Integrin-linked protein kinase Ras-related protein Rab-18 Ras-relatedprotein Rab-18 RNA-binding protein 3 RNA-binding protein 3 Annexin A314-3-3 protein eta Eosinophil peroxidase 26S protease regulatory subunit8 Nuclear ubiquitous casein and cyclin-dependent kinase substrate 14-trimethylaminobutyraldehyde Heterogeneous nuclear dehydrogenaseribonucleoprotein H3 60S ribosomal protein L29 116 kDa U5 small nuclearribonucleoprotein component 60S ribosomal protein L35a 40S ribosomalprotein S28 6-phosphogluconolactonase Aconitate hydratase, mitochondrial78 kDa glucose-regulated protein Antigen peptide transporter 1 Adipocyteplasma membrane- Coronin-1C associated protein Alpha-aminoadipicsemialdehyde Eukaryotic translation initiation dehydrogenase factor 3subunit A Apolipoprotein A-I Eukaryotic translation initiation factor 3subunit E Calnexin Eukaryotic translation initiation factor 4BCalreticulin Fibrinogen alpha chain Cellular nucleic acid-bindingprotein Galectin-3 Chloride intracellular channel Haptoglobin protein 1Cleavage and polyadenylation Heterogeneous nuclear specificity factorsubunit 5 ribonucleoprotein L Collagen alpha-2(VI) chain Heterogeneousnuclear ribonucleoproteins A2/B1 Coronin-1A Hypoxanthine-guaninephosphoribosyltransferase Cytochrome c oxidase subunit 5A, KHdomain-containing, RNA- mitochondrial binding, signal transduction-associated protein 1 Eukaryotic translation initiation factor Lamininsubunit gamma-1 2 subunit 1 Eukaryotic translation initiation factorLeukocyte elastase inhibitor 4H FACT complex subunit SPT16 LIM and SH3domain protein 1 Filamin-C Myeloid cell nuclear differentiation antigenFour and a half LIM domains protein Myosin regulatory light chain 12A 1Heat shock protein 105 kDa Non-POU domain-containing octamer-bindingprotein Heterogeneous nuclear Nucleolin ribonucleoprotein A0Lactotransferrin Obg-like ATPase 1 Lamina-associated polypeptide 2,Protein transport protein Sec23A isoforms beta/gamma Matrin-3Protein-L-isoaspartate O- methyltransferase Moesin Puromycin-sensitiveaminopeptidase Nucleolar and coiled-body rRNA 2′-O-methyltransferasephosphoprotein 1 fibrillarin Nucleolysin TIARSerine/arginine-rich-splicing factor 7 PDZ and LIM domain protein 5Signal recognition particle 9 kDa protein Peptidyl-prolyl cis-transisomerase Small nuclear ribonucleoprotein- FKBP4 associated proteins Band B′ Perilipin-3 Splicing factor 3A subunit 3Phosphatidylethanolamine-binding T-complex protein 1 subunit betaprotein 1 Prelamin-A/C Thyroid hormone receptor- associated protein 3Protein canopy homolog 2 Transportin-1 Protein NipSnap homolog 1 Proteinphosphatase 1G Protein transport protein Sec61 subunit betaProtein-tyrosine-phosphatase Regulator of nonsense transcripts 1Septin-9 S-formylglutathione hydrolase Signal recognition particle 14kDa protein Succinate dehydrogenase [ubiquinone] flavoprotein subunit,mitochondrial Translocon-associated protein subunit delta Tubulin betachain Tyrosine--tRNA ligase, cytoplasmic U1 small nuclearribonucleoprotein A Ubiquitin carboxyl-terminal hydrolase 7 UMP-CMPkinase Vinculin Nicotinamide phosphoribosyltransferase Annexin A3Eosinophil peroxidase Nuclear ubiquitous casein and cyclin- dependentkinase substrate 1 14-3-3 protein gamma 26S protease regulatory subunit4 AGR2 Cathepsin B EMILIN-1 Glutathione S-transferase omega-1 Heat shockprotein HSP 90-alpha Heat shock protein HSP 90-beta Hydroxysteroiddehydrogenase-like protein 2 Mycophenolic acid acyl-glucuronideesterase, mitochondrial Proteasome activator complex subunit 1Ras-related protein Rab-1A Ras-related protein Ral-B RNA-binding protein14 Septin 11, isoform CRA_b Tubulin-specific chaperone AMetallothionein-2 B-cell receptor-associated protein 31 Ras-relatedprotein Rab-5C Stress-induced-phosphoprotein 1

CD patient PCDAI severity scores showed significant correlation with 83proteins, 10% of which are components of the protein ubiquitinationpathway. In contrast, 10% of the 43 proteins that correlate with UCpatient PUCAI scores are components of the mTOR signaling pathway. 15 ofthe CD-associated and 9 of the UC-associated proteins are regulated byHNF4A which was identified in a pediatric population to be associatedwith CD (Genes Immun 2012; 13:556-65) and is a UC susceptibility loci(Nat Genet 2009; 41:1330-4). There were eight proteins that correlatewith severity score in both CD and UC patients, including RNA bindingand integrin signaling proteins. Of the 118 proteins showing correlationwith severity, 39 proteins were identified as biomarker candidates, fourof which were in the panels for diagnosis or differentiation. Amongstthe proteins biomarkers for control vs IBD the relative expression ofboth inorganic phosphatase and visfatin show significant correlationwith CD severity (FIG. 3 A, B). Similarly, amongst the proteinsbiomarkers for UC v. CD, the relative expression proteinmetallothionein-2 (MT2) correlates with CD severity (FIG. 3C), whereasHNRP H3 is inversely related to UC severity (FIG. 3D). A previous studyfound a correlation between MT2 and grade of inflammation in adult IBDbiopsies (J Pathol 2014; 233:89-100); the correlation with diseaseseverity of the other 3 proteins is a new finding.

ELISA of visfatin and MT2 are consistent with proteomic data.

With the ultimate intent of translating our findings into the clinicalsetting, the absolute amount of two candidate biomarkers (one from eachof the panels) were measured from patient biopsy samples. Usingcommercially available kits, the amount of visfatin and MT2 in a subsetof validation cohort patient biopsies were measured by ELISA. The amountof visfatin was within the detection limits for 23/24 samples tested.The relative amounts of vistafin determined by proteomics in thediscovery cohort is consistent in the validation cohort the ELISA (FIG.4A), with a significantly higher amount in IBD patients. Similarly, MT2was quantified in all samples tested from the validation cohort, and wassignificantly higher in CD than in UC patients in the validationproteomic and ELISA analyses (FIG. 4B). Consistent with the discoverycohort proteomic data, the ELISA results of the validation cohort showedcorrelation between the absolute amount of MT2 and the PCDAI in moderateor severe (PCDAI>30) CD patients (FIG. 4C). Due to the limited number ofpatients with mild CD, it cannot be determined whether the single mildCD patient with elevated MT2 levels is an outlier.

Example 2

The following figures that will now be described show the relativeabundance of proteins in IBD, UC, CD as well as for different degree ofseverity of the disease that were identified by a variety of statisticalmodels.

In an exemplary analysis 1949 proteins were accurately quantified fromthe patient biopsies; about 50% of these were found to be significantlydifferent between patient groups by ANOVA. 296 proteins were determinedby t-test to be significantly different between CD and UC patients;principle component analysis of resulted in segregation of control, CDand UC patient groups.

FIGS. 5 and 6 show a number of proteins that are more abundant in CD andUC affected individuals than normal controls. FIG. 7 shows an additionalanalysis where calumenin, LAP3 and B-CK are identified as biomarkers forpediatric IBD.

FIG. 8 shows a number of proteins that exhibit a differential abundancein CD and UC patients.

FIG. 9 shows a number of proteins that exhibit a differential abundancein patients with different levels of UC disease severity.

FIG. 10 shows a number of proteins that exhibit a differential abundancein patients with different levels of CD disease severity.

In yet another analysis FIG. 11 shows proteins identified by PrincipalComponent Analysis (PCA) that exhibit differential abundance in controlvs CD vs UC and provide examples of potential protein markers from thisanalysis.

Another example of proteins identified by PCA of which 418 proteins thatare significantly different by Ttest between CD and UC patients wereused. The list of 77 proteins that are most responsible for PCA groupingwere identified and considered potential biomarkers. FIG. 12 providesexamples of potential protein markers from this analysis.

In yet another analysis, the segregation of CD vs UC was analyzed usingRoccet. ROC curves were generated by Monte-Carlo cross validation (MCCV)using balanced subsampling. In each MCCV, two thirds (⅔) of the (max)important features are then used to build classification models which isvalidated on ⅓ of the samples that were left out. The procedures wererepeated multiple times to calculate the performance and confidenceinterval of each model. A similar analysis was performed usingROC/Partial Least Squares Discriminant Analysis (PLSDA). Similaranalyses were performed to show the segregation of controls vs disease(IBD), control vs CD and control vs UC. FIGS. 13, 14, 15, 16 and 17 showexamples of protein markers identified using this analysis.

A further exemplary analysis was performed using ROC that shows theelevated levels of certain proteins in IBD (FIG. 18).

The diagnostic markers described above can be used in a method forclassifying a sample as being associated with IBD, UC or CD. The methodcomprises the steps of determining a presence or level of one or more ofthe diagnostic markers and comparing the presence or level to samplesfrom IBD, UC or CD patients and/or normal patients. A combination ofdiagnostic markers may be used and may also further be combined with astandard diagnostic results derived from a disease activity index.

There is also provided a method for treating IBD or UC or CD diseasewherein a diagnosis is first established using one or more of thedisease markers described above and determining a course of treatment.The treatment may consist in administering to the patient apharmaceutically effective amount of a compound selected fromaminosalycylates, immunomodulators, anti-integrins, anti-cytokines,enteral feed programs, steroids, corticosteroids, antibiotics,anti-TNFα, bismuth or a combination thereof.

The following is an exemplary protocol for mass-spec analysis used toidentify markers. It will be appreciated that the person skilled in theart may implement modifications of this protocol in order to adapt it toparticular situations or sample characteristics without deviating fromthe invention.

Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC):

Human hepatic HuH7 cells (HuH-7), human embryonic kidney 293 cells(HEK-293) and human colorectal cancer 116 cells (HCT-116) wereindividually grown at 37° C. in a 5% CO2 humidified incubator. SILACmedium was prepared as follows: DMEM lacking lysine, arginine andmethionine was custom prepared by AthenaES (Baltimore, Md., USA) andsupplemented with 30 mg/L methionine (Sigma Aldrich; Oakville, ON, CAN),10% (v/v) dialyzed FBS (GIBCO-Invitrogen; Burlington, ON,CAN), 1 mMsodium pyruvate (Gibco-lnvitrogen), 28 μg/mL gentamicin(Gibco-Invitrogen), and[¹³C₆, ¹⁵N₂]-L-lysine, [¹³C₆, ¹⁵N₄]-L-arginine(heavy form of amino acids; Heavy Media) from Sigma Aldrich (Oakville,ON, CAN) at final concentrations of 42 mg/L and 146 mg/L for arginineand lysine respectively. For HCT-116, the concentration of arginine wasincreased to 84 mg/L. Cells were grown for at least 10 doublings inSILAC media to allow for complete incorporation of the isotopicallylabeled amino acids into the cells.

Determination of the rate of SILAC amino acids incorporation into HuH-7,HEK-293 and HCT-116 cells:

Cells were grown to 80% confluency in SILAC medium (5×10⁶ cells wereplated in 10-cm dish). Next, the cells were washed twice with ice-coldphosphate-buffered saline and lyzed by addition of 1 mL of 1×RIPA buffer(50 mM Tris (pH 7.6), 150 mM NaCl, 1% (v/v) NP-40, 0.5% (w/v)deoxycholate, 0.1% (w/v) SDS with protease inhibitor cocktail (CompleteMini Roche; Mississauga, ON,CAN) and phosphatase inhibitor (PhosStopRoche tablet). The lysates were then transferred to 15 mL conical tubesand the proteins were precipitated by addition of 5 mL ice-cold acetonefollowed by incubation at −20° C. overnight. Proteins were collected bycentrifugation (3000×g, 10 min, 4° C.), washed with ice-cold acetone twotimes, and the protein pellets were resolubilized in 300 μL of a 50 mMNH₄HCO₃ solution containing 8 M urea. Protein concentrations weredetermined by the Bradford dye-binding method using Bio-Rad's ProteinAssay Kit (Mississauga, ON, CAN). For the general in-solution digestion,200 μg of protein lysates were reconstituted in 50 mM NH₄HCO₃ (200 μL)and proteins were reduced by mixing with 5 μL of 400 mM DTT at 56° C.for 15 min. The proteins were then subjected to alkylation by mixingwith 20 μL of 400 mM iodoacetamide in darkness (15 min at roomtemperature) followed by addition of 800 μL of 50 mM NH₄HCO₃ to reducethe urea concentration to ˜0.8 M. Next, the proteins were digested withTPCK-trypsin solution (final ratio of 1:20 (w/w, trypsin:protein) at 37°C. for 18 h. Finally, the digested peptides were desalted using C₁₈Sep-Pack cartridges (Waters), dried down in a speed-vac, andreconstituted in 0.5% formic acid prior to mass spectrometric analysis(as described below) and the determination of labeling efficiency. Theincorporation efficiency was calculated according to the followingequation: (1−1/Ratio(H/L)); where H and L represents the intensity ofheavy and light peptides detected by mass-spectrometry, respectively.Labeling was considered complete when values reached at least 95% foreach cell type.

Proteomic Analysis of Biopsies Using Super-SILAC-Based Quantitative MassSpectrometry:

Biopsies were lysed in 4% SDS (sodium dodecyl sulfate), 50 mM Tris-HCl(pH 8.0) supplemented with proteinase inhibitor cocktail (Roche) andhomogenized with a Pellet pestle. The lysates were sonicated 3 timeswith 10 s pulses each with at least 30 s on ice between each pulse.Protein concentrations were determined using the Bio-Rad DC ProteinAssay. The proteins were processed using the Filter Aided SamplePreparation Method (FASP) as previously described with somemodifications. Colon tissue lysates (45 μg of proteins) and heavySILAC-labeled cell lysates (15 μg from each HuH-7, HEK-293 and HCT-116cells) were mixed at a 1:1 weight ratio and transferred into the filter.The samples were centrifuged (16,000×g, 10 min), followed by two washesof 200 μL 8 M urea, 50 mM Tris-HCl pH 8.0. Samples were then reduced byincubation in 200 μL of 8 M urea, 50 mM Tris-HCl (pH 8.0) supplementedwith 20 mM dithiothreitol. After centrifugation, samples were subjectedto alkylation by adding 200 μL of 8 M urea, 50 mM Tris-HCl pH 8.0,containing 20 mM iodoacetamide (30 min at room temperature protectedfrom light). Samples were washed using 200 μL 8 M urea, 50 mM Tris-HClpH 8.0 (twice) to remove excess SDS. To further dilute urea, two washesof 200 μL 50 mM Tris-HCl pH 8.0 were performed. For the trypsin digest,samples were incubated in 200 μL of 50 mM Tris-HCl pH 8.0, containing 5μg of Trypsin (TPCK Treated, Worthington) on a shaker (250 rpm) at 37°C. overnight. Finally, 200 μL of 50 mM Tris-HCl pH 8.0 was added toelute the peptides by centrifugation (twice). Peptides werefractionated, using an in-house constructed SCX column with five pHfractions (pH 4.0, 6.0, 8.0, 10.0, 12.0). The buffer composition was 20mM boric acid, 20 mM phosphoric acid, and 20 mM acetic acid, with the pHadjusted by using 1 M NaOH). Finally, the fractionated samples weredesalted using in-house C₁₈ desalting cartridges and dried in aspeed-vac prior to LC-MS analysis.

Mass-Spectrometry Analyses:

All resulting peptide mixtures were analyzed by high-performance liquidchromatography/electrospray ionization tandem mass spectrometry(HPLC-ESI-MS/MS). The HPLC-ESI-MS/MS consisted of an automated Ekspert™nanoLC 400 system (Eksigent, Dublin, Calif., USA) coupled with an LTQVelos Pro Orbitrap Elite mass spectrometer (ThermoFisher Scientific, SanJose, Calif.) equipped with a nano-electrospray interface operated inpositive ion mode. Briefly, each peptide mixture was reconstituted in 20μL of 0.5% (v/v) formic acid and 12 μL was loaded on a 200 μm×50 mmfritted fused silica pre-column packed in-house with reverse phase MagicC₁₈AQ resins (5 μm; 200 Å pore size; Dr. Maisch GmbH, Ammerbuch,Germany). The separation of peptides was performed on an analyticalcolumn (75 μm×10 cm) packed with reverse phase beads (3 μm; 120 Å poresize; Dr. Maisch GmbH, Ammerbuch, Germany) using a 120 min gradient of5-30% acetonitrile (v/v) containing 0.1% formic acid (v/v) (JT Baker,Phillipsburg N.J., USA) at an eluent flow rate of 300 nL/min. The sprayvoltage was set to 2.2 kV and the temperature of heated capillary was300° C. The instrument method consisted of one full MS scan from 400 to2000 m/z followed by data-dependent MS/MS scan of the 20 most intenseions, a dynamic exclusion repeat count of 2, and a repeat duration of 90s. The full mass was scanned in an Orbitrap analyzer with R=60,000(defined at m/z 400), and the subsequent MS/MS analyses were performedin LTQ analyzer. To improve the mass accuracy, all the measurements inthe Orbitrap mass analyzer were performed with on-the-fly internalrecalibration (“Lock Mass”). The charge state rejection function wasenabled with charge states “unassigned” and “single” states rejected.All data were recorded with Xcalibur software (ThermoFisher Scientific,San Jose, Calif.).

Database Search and Bioinformatic Analysis:

Raw files can be processed and analyzed by MaxQuant, Version 1.5.1against the decoy Uniport-human database (downloaded 2014 Jul. 11),including commonly observed contaminants. The protein-group file wasimported into Persus (version 1.3.0.4) for data statistical analysis.

What is claimed is:
 1. A method of detecting inflammatory bowel diseasein a subject comprising: providing a sample obtained from the gut of asubject; detecting in said sample a level of each of the followingmarkers: fatty acid-binding protein, visfatin, UDP-Glucose6-dehydrogenase, leucine-rich PPR motif-containing protein, andinorganic pyrophosphatase.
 2. A method for treating inflammatory boweldisease in a patient comprising: determining whether inflammatory boweldisease has been detected in said subject by performing the method ofclaim 1 and administering to said patient a compound pharmaceuticallyeffective against inflammatory bowel disease.
 3. The method of claim 2,wherein said administering comprises administering a pharmaceuticallyeffective amount of a compound selected from the group consisting ofaminosalycylates, immunomodulators, anti-integrins, anti-cytokines,enteral feed programs, steroids, corticosteroids, antibiotics,anti-TNFa, and bismuth, or a combination thereof.
 4. The method of claim1, further comprising obtaining further information regarding thepresence of inflammatory bowel disease in said subject by combining saiddetecting of said markers with a disease activity index specific forinflammatory bowel disease.
 5. The method of claim 1, wherein saidobtaining a level is by using an immunoassay.
 6. The method of claim 5,wherein said immunoassay is an ELISA.
 7. A method for treatinginflammatory bowel disease in a patient comprising: requesting ananalysis of a sample according to the method of claim 1 andadministering to the patient a compound selected from the groupconsisting of aminosalycylates, immunomodulators, anti-integrins,anti-cytokines, enteral feed programs, steroids, corticosteroids,antibiotics, anti-TNFα, and bismuth, or combinations thereof, if thesample is associated with inflammatory bowel disease.